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Rotman Insights Hub | University of Toronto - Rotman School of Management

A leader’s guide to safely reopening the workplace

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Ajay Agrawal, Joshua Gans, Avi Goldfarb, Mara Lederman

Perhaps the single biggest implication of reopening national economies is that responsibility and thus liability for dealing with the COVID-19 pandemic will shift from the public to the private sector. Fortune 500 CEOs and small business owners alike will soon be making decisions that affect the health not only of their business but also their people (employees, contractors, customers, suppliers) — which in turn will affect the health of their families, friends and neighbours. With so much at stake, how should business leaders plan for operating in the post-stay-at-home phase of the recovery? In this article we will present a simple but powerful framework for creating a plan.

Two types of solutions

The current crisis is driven by a health problem: We don’t yet have a treatment or a vaccine for the novel coronavirus. Managers have little control over that, but until the health problem is solved, places of work will be opportunities for infected people to infect others. This creates a management problem requiring management solutions — and managers do have control of those.

The management problem at hand is whether and how to reopen businesses, given that workplace spread of the virus remains a real threat. This management problem is caused by an information gap: We don’t know who has the virus (i.e. is infectious), who had it (i.e. is immune) and who has never had it (i.e. is susceptible). If we had that information, there would be no economic crisis. We would simply require infectious people to quarantine while the vast majority of us who are healthy go about life as usual. Not having this information is costing us, by one estimate, $375 billion a month globally. In the absence of this information, beginning an economic recovery requires us to solve the management problem.

Lockdown is the most extreme always-on solution; reopening requires more nuanced ones.

There are two types of solutions to this problem. First, information-based solutions involve predicting who is infectious and who is immune and then using this information to decide who gets to enter the workplace. Second — since these predictions will inevitably be imperfect — are always-on solutions: technologies and processes that limit the spread of the virus when infectious people do enter. Lockdown is the most extreme always-on solution; reopening requires more nuanced ones. Let’s take a closer look at these two categories of solutions.

1) Information-based solutions

There are a variety of ways to collect information on who is likely to be infectious. Most obviously, people can be tested for the novel coronavirus (e.g. with nasopharyngeal swabs). These tests can sometimes be quite unreliable, they are not always readily available and getting results can take days. Still, over time, this situation should improve. Eventually, we anticipate, organizations will be doing widespread, frequent employee testing.

Another form of information collection is monitoring symptoms, especially mild ones that the patient may not even notice. In some countries people already have their temperature checked before they’re allowed to enter an office, restaurant, airplane, or subway. This is useful but imperfect: Some people with fevers will not have the coronavirus, while others without fevers may nevertheless be infected. Combining temperature checks with other diagnostic information such as hospital-based chest x-rays and blood oxygen levels may improve accuracy. These forms of information collection may be less accurate than direct tests for the virus, but they may be cheaper, faster and easier for employers to implement regularly and at scale.

There are also ways to monitor different parts of your workplace for signs of an outbreak, even if you don’t know who is infected. For instance, sensors are being developed that could detect the coronavirus in the air. Other tests can pick up traces of it in sewage. Machine-learning tools could combine these and information from other sensors to predict the likelihood that someone in a building or neighbourhood is infected and order individual tests for everyone there. In our 2018 book Prediction

Machines, we described how advances in artificial intelligence enable increasingly complex predictions from a wide variety of data sources such as these.

The trouble is, information-based solutions are probabilistic, and some errors are inevitable. Credit card fraud is a good example. Suppose a bank receives a warning that a credit card transaction is one per cent likely to be fraudulent. Should the bank deny the transaction or allow it to proceed? How should this depend on the profitability of the customer to the bank?

So it is with coronavirus: Should your business keep operating if there is a one per cent chance an infected person gets through the door? What about a five per cent chance or a 0.1&percnt chance? The answer depends on the benefits relative to the costs — on the importance of opening the physical workplace versus the risk of infection. Indeed, this is why supermarkets, pharmacies, and other essential businesses have remained open throughout the crisis with effectively no information-based solution: because the benefits of remaining open are so obviously large. On the other hand, many professional services firms can function quite well remotely, so their physical workplaces remain shut.

Even if you can’t reduce to zero the likelihood that the virus will enter the workplace, you can limit its impact should it gain entry. Enter always-on solutions.

2) Always-on solutions

Until the information-management solutions we’ve discussed thus far ramp up, always-on solutions will be the primary approach managers use to reopen their businesses.

All sorts of decisions that previously would have been made on the basis of productivity and efficiency now need to also consider the possibility of infection. For example, in the restaurant industry, the flow of people in and out of the kitchen is now an infection-risk management problem. In the retail fashion industry, decisions about whether to open changing rooms or allow customers to try items on are now infection-risk management problems. At the same-time, moving from physical to digital documents now reduces infection risk as well increasing efficiency and wasting less paper. The risk of transferring the virus by exchanging cash increases the relative benefits of digital payment systems.

To date, we have seen two broad types of always-on solutions. The first kind do not change the number or nature of interactions but aim to make those interactions less risky. Things like masks, hand sanitizer stations, and plexiglass screens at reception desks and store checkouts all fall into this category.

The second kind are solutions that aim to make people interact less. These include redesigned physical spaces (to minimize interactions or high-touch surfaces), redesigned workflows (to enable work to be done in parallel or sequence rather than jointly), and redesigned people-management processes (to minimize interactions across groups or teams). Reductions in capacity — whether of employees (through layoffs and furloughs) or customers (through limits on occupancy) — fall into this category as well.

Sensors are being developed that could detect the coronavirus in the air.

Always-on solutions impose additional costs on business. There are direct costs for things like protective equipment and more frequent cleaning. If the always-on solution involves reduced capacity, profits will fall. Finally, reengineered spaces, workflows, and processes may lead to lower productivity, greater inefficiency, or unhappier workers. Of course, certain changes could increase productivity. Some businesses, especially those in congested cities like New York, report having people that work from home has made them more productive, mainly because it eliminates long commutes.

Different types of businesses lend themselves differently to always-on solutions. It’s easier to maintain social distancing in a garden centre than in a hair salon. As a result, even if they are allowed to some businesses are choosing not to open. Many restaurants have elected to keep their dine-in services closed because with social distancing, they can’t allow in enough customers at a time to offset the costs of cleaners and wait staff.

In closing

As a manager, you are responsible for crafting your organization’s information-based and always-on solutions. It is up to you to decide how much information to collect about who is infectious and immune; how to collect that information and how often; and how to act on it, based on how much risk your organization is willing to bear.

You must also decide how your day-to-day processes should change to limit the spread of disease should an infected person arrive in your workplace, and consider how those changes will affect both safety and productivity. Clearly, there is no point in bringing workers back to the office if always-on solutions prevent them from doing their jobs any better than they would at home. Together, these decisions will determine whether your business can survive and thrive while we wait for a treatment or a vaccine. These decisions involve calculated trade-offs, an understanding of risk and a willingness to innovate.

In the next phase of the COVID-19 recovery, many CEOs of large enterprises will begin to behave like presidents and prime ministers. They will report their numbers of infections and deaths, explain their strategies for keeping their curves flat, decide how quickly to ease isolation measures and swing into crisis management mode when there’s an outbreak. Some will be more like the U.S., others more like Sweden. The outliers, those that choose unusual strategies or experience more infections than their peers, will be scrutinized. Their challenge is that every decision involves a trade-off between short-term profit and safety, and therefore assumes some risk. If tragedy strikes, as it likely will for some, the central question will not be who is to blame but whether the risk they took was wise.

This article originally appeared in MIT’s Technology Review and was republished in the Fall 2020 issue of Rotman Management magazine

Ajay Agrawal is the Geoffrey Taber chair in entrepreneurship and innovation and professor of entrepreneurship at the Rotman School of Management. He founded the Creative Destruction Lab (CDL), a non-profit organization with eight global locations that delivers an objectives-based program for massively scalable, seed-stage, science- and technology-based companies.
Joshua Gans is the Jeffrey S. Skoll chair of technical innovation and entrepreneurship and professor of strategic management at the Rotman School of Management (with a cross-appointment to the University of Toronto’s department of economics). He is also chief economist at CDL. His latest book is Economics in the Age of COVID-19 (MIT Press, 2020).
Avi Goldfarb is the Rotman chair in artificial intelligence and healthcare, Ellison professor of marketing at the Rotman School of Management and chief data scientist at CDL. Professors Agrawal, Gans and Goldfarb are the co-authors of Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Review Press, 2018).
Mara Lederman is a professor of strategic management at the Rotman School, academic lead, CDL Partners Program and CDL Toronto site lead.